This research will develop a process for modeling the prevalence and outcomes of traumatic brain injury (TBI) among nursing home residents. Specifically, it will (1) result in a method for measuring the prevalence of TBI among nursing home residents throughout the country; (2) develop a database that can be used to measure and compare the prevalence of TBI among nursing home residents living in different geographic regions, defined in a variety of ways; (3) enable the user to identify prevalence of TBI among people newly admitted to nursing homes, and to identify the onset of TBI among on-going residents of the nursing home; and (4) identify the factors associated with improved function and likely discharge to the community among nursing home residents with TBI. Secondary data will be used. The Minimum Data Set (MDS) will provide information about characteristics of nursing facility residents across the county, including health, functional, behavioral, and cognitive status. The MDS will be used to identify individuals with TBI at admission, as well as those who develop a TBI while residing in the nursing facility; to profile the characteristics of individuals with TBI; and to model likely improvement and discharge. The Online Survey, Certification, and Reporting (OSCAR) data will provide information about the nursing facility, including the county in which it is located and staffing levels by type of personnel. The Area Resource File (ARF) will provide information about the market characteristics, and provide identifiers of various geographic divisions. All of these databases contain data for the entire nation. Descriptive analyses will be used to profile residents with TBI. A statistical model will be developed to identify people with likely new TBI while resident in a nursing home. Various regression techniques will be used to model outcomes for people with TBI, including changes in function and community discharge.